我生成一个如下所示的数据框（summaryDF）：
accuracy f1 precision recall
0 0.494 0.722433 0.722433 0.722433
0 0.290 0.826087 0.826087 0.826087
0 0.274 0.629630 0.629630 0.629630
0 0.278 0.628571 0.628571 0.628571
0 0.288 0.718750 0.718750 0.718750
0 0.740 0.740000 0.740000 0.740000
0 0.698 0.765133 0.765133 0.765133
0 0.582 0.778547 0.778547 0.778547
0 0.682 0.748235 0.748235 0.748235
0 0.574 0.767918 0.767918 0.767918
0 0.398 0.711656 0.711656 0.711656
0 0.530 0.780083 0.780083 0.780083
因为我知道这行中的每一行应该是什么，然后我使用这段代码来设置每一行的名称（这些不是实际的行名，只是出于参数的缘故）。
summaryDF = summaryDF.set_index(['A','B','C', 'D','E','F','G','H','I','J','K','L'])
然而，我得到了：
level = frame[col].values
File "/Users/me/anaconda/lib/python2.7/site-packages/pandas/core/frame.py", line 1797, in __getitem__
return self._getitem_column(key)
File "/Users/me/anaconda/lib/python2.7/site-packages/pandas/core/frame.py", line 1804, in _getitem_column
return self._get_item_cache(key)
File "/Users/me/anaconda/lib/python2.7/site-packages/pandas/core/generic.py", line 1084, in _get_item_cache
values = self._data.get(item)
File "/Users/me/anaconda/lib/python2.7/site-packages/pandas/core/internals.py", line 2851, in get
loc = self.items.get_loc(item)
File "/Users/me/anaconda/lib/python2.7/site-packages/pandas/core/index.py", line 1572, in get_loc
return self._engine.get_loc(_values_from_object(key))
File "pandas/index.pyx", line 134, in pandas.index.IndexEngine.get_loc (pandas/index.c:3824)
File "pandas/index.pyx", line 154, in pandas.index.IndexEngine.get_loc (pandas/index.c:3704)
File "pandas/hashtable.pyx", line 686, in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12280)
File "pandas/hashtable.pyx", line 694, in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12231)
KeyError: 'A'
我不知道我做错了什么，并且进行了广泛的研究。有什么想法吗？
答案 0 :(得分：5)
我猜你和@jezrael误解了熊猫文档中的一个例子：
A B和In [55]: df = pd.DataFrame(np.random.randint(0, 10, (5,4)), columns=list('ABCD'))
In [56]: df
Out[56]:
A B C D
0 6 9 7 4
1 5 1 3 4
2 4 4 0 5
3 9 0 9 8
4 6 4 5 7
In [57]: df.set_index(['A','B'])
Out[57]:
C D
A B
6 9 7 4
5 1 3 4
4 4 0 5
9 0 9 8
6 4 5 7
是此示例中的列名称/标签：
In [58]: df.set_index([['A','B','C','D','E']])
Out[58]:
A B C D
A 6 9 7 4
B 5 1 3 4
C 4 4 0 5
D 9 0 9 8
E 6 4 5 7
documentation表示列标签/ 数组 的 列表。
所以你在寻找：
df.index = ['A','B',...]但是@jezrael建议/**
* Unlike {@link FloatingActionButton#show()} animates button even it not currently
* laid out
* @param fab fab to show
*/
@SuppressLint("NewApi")
public static void show(FloatingActionButton fab) {
if (ViewCompat.isLaidOut(fab) ||
Build.VERSION.SDK_INT < Build.VERSION_CODES.ICE_CREAM_SANDWICH) {
fab.show();
} else {
fab.animate().cancel();//cancel all animations
fab.setScaleX(0f);
fab.setScaleY(0f);
fab.setAlpha(0f);
fab.setVisibility(View.VISIBLE);
//values from support lib source code
fab.animate().setDuration(200).scaleX(1).scaleY(1).alpha(1)
.setInterpolator(new LinearOutSlowInInterpolator());
}
}
是更快更惯用的方法......
答案 1 :(得分：2)
如果list的{{1}}与summaryDF.index的{{1}}相同，则需要将length分配给list：
length<强>计时：
DataFrame另一种解决方案是将summaryDF.index = ['A','B','C', 'D','E','F','G','H','I','J','K','L']
print (summaryDF)
accuracy f1 precision recall
A 0.494 0.722433 0.722433 0.722433
B 0.290 0.826087 0.826087 0.826087
C 0.274 0.629630 0.629630 0.629630
D 0.278 0.628571 0.628571 0.628571
E 0.288 0.718750 0.718750 0.718750
F 0.740 0.740000 0.740000 0.740000
G 0.698 0.765133 0.765133 0.765133
H 0.582 0.778547 0.778547 0.778547
I 0.682 0.748235 0.748235 0.748235
J 0.574 0.767918 0.767918 0.767918
K 0.398 0.711656 0.711656 0.711656
L 0.530 0.780083 0.780083 0.780083
print (summaryDF.index)
Index(['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L'], dtype='object')
转换为In [117]: %timeit summaryDF.index = ['A','B','C', 'D','E','F','G','H','I','J','K','L']
The slowest run took 6.86 times longer than the fastest. This could mean that an intermediate result is being cached.
10000 loops, best of 3: 76.2 µs per loop
In [118]: %timeit summaryDF.set_index(pd.Index(['A','B','C', 'D','E','F','G','H','I','J','K','L']))
The slowest run took 6.77 times longer than the fastest. This could mean that an intermediate result is being cached.
1000 loops, best of 3: 227 µs per loop
：
list